Hierarchical Incentive Mechanism Design for Federated Machine Learning in Mobile Networks

Wei Yang Bryan Lim, Zehui Xiong, Chunyan Miao, Dusit Niyato, Qiang Yang, Cyril Leung, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

142 Scopus citations


In recent years, the enhanced sensing and computation capabilities of Internet-of-Things (IoT) devices have opened the doors to several mobile crowdsensing applications. In mobile crowdsensing, a model owner announces a sensing task following which interested workers collect the required data. However, in some cases, a model owner may have insufficient data samples to build an effective machine learning model. To this end, we propose a federated learning (FL)-based privacy-preserving approach to facilitate collaborative machine learning among multiple model owners in mobile crowdsensing. Our system model allows collaborative machine learning without compromising data privacy given that only the model parameters instead of the raw data are exchanged within the federation. However, there are two main challenges of incentive mismatches between workers and model owners, as well as among model owners. For the former, we leverage on the self-revealing mechanism in the contract theory under information asymmetry. For the latter, to ensure the stability of a federation through preventing free-riding attacks, we use the coalitional game theory approach that rewards model owners based on their marginal contributions. Considering the inherent hierarchical structure of the involved entities, we propose a hierarchical incentive mechanism framework. Using the backward induction, we first solve the contract formulation and then proceed to solve the coalitional game with the merge and split algorithm. The numerical results validate the performance efficiency of our proposed hierarchical incentive mechanism design, in terms of incentive compatibility of our contract design and fair payoffs of model owners in stable federation formation.

Original languageEnglish (US)
Article number9057543
Pages (from-to)9575-9588
Number of pages14
JournalIEEE Internet of Things Journal
Issue number10
StatePublished - Oct 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


  • Artificial intelligence
  • federated learning (FL)
  • incentive mechanism
  • mobile crowdsensing
  • mobile networks


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